| Proj No. | A3209-251 |
| Title | Machine Learning-Based Classification of Antimicrobial Resistance Using the Comprehensive Antibiotic Resistance Database |
| Summary | This project aims to develop a machine learning model to classify antimicrobial resistance (AMR) genes using data from the Comprehensive Antibiotic Resistance Database (CARD). The student will extract genomic sequences and associated resistance phenotypes from CARD (https://card.mcmaster.ca/) and preprocess the data to create feature vectors suitable for machine learning algorithms. Various classifiers, such as support vector machines, random forests, and neural networks, will be implemented and evaluated to determine their effectiveness in accurately predicting resistance profiles. The project will involve training and testing these models, followed by performance assessment using metrics like accuracy, precision, recall, and F1-score. The outcome will be a comparative analysis of machine learning techniques in classifying AMR genes, contributing to improved detection and understanding of antimicrobial resistance mechanisms. |
| Supervisor | A/P Wang Lipo (Loc:S1 > S1 B1C > S1 B1C 98, Ext: +65 67906372) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering I (Loc: S2-B4c-15) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |